The invention relates generally to a filtering scheme for noise reduction in Surface Plasmon Resonance (SPR) sensorgrams and more particularly, to an adaptive linear filtering scheme for noise reduction in SPR sensorgrams.
A surface plasmon resonance (SPR) measurement system typically presents detected changes in the refractive index of a sample in the form of a sensorgram. A sensorgram is a biomolecular interaction plot of the relative refractive index of the sample versus time and may contain one or more phases. Each phase of the plot includes a buffer-only period followed by association and dissociation periods. The association and dissociation periods include adsorption and desorption of biomolecules resulting in a change in refractive index. The adsorption-desorption can be followed in real-time and the amount of adsorbed species can be determined. The SPR sensorgrams may contain different types of noise components that can mask or otherwise distort features of the sensorgrams. The noise components may be attributed to measurement uncertainty in an optical apparatus of the SPR measurement system. The noise components may also be due to mechanical events such as the opening or closing of valves that control the flow of buffer and analytes in the samples. Furthermore, there may be drift in the measurements due to temperature variations or sample non-uniformities.
Various schemes have been investigated for noise reduction in SPR sensorgrams, including linear and nonlinear filtering. Linear filtering can be very effective in reducing random noise components present in a signal. However, convention linear filtering has been noted to have several shortcomings. When conventional linear filtering, such as lowpass filtering, is applied to an SPR sensorgram, high frequency features, such as sharp transitions in the sensorgram, may be smoothed out, or eliminated. Yet these sharp transitions may be indicative of a critical biochemical process or event, such as the onset of a binding event between analytes and ligands within the sample. Smoothing out or eliminating these sharp transitions can make determination of association/dissociation rates and other important indicators of biochemical processes more difficult or less accurate. Conventional linear filtering can also result in ringing when a signal includes discontinuities or other anomalies, making biochemical processes or events depicted in the SPR sensorgram difficult to interpret.
Therefore, it is desirable to have a linear filtering method for reducing noise while preserving important signal characteristics in SPR sensorgrams.
Sensorgram filtering is used to reduce high frequency noise that is random in its nature. E.g. air bubble outliers and steps should not be reduced due to filtering. Sensorgram filtering shall not affect kinetic constants.